Using data mining as a strategy for assessing asynchronous discussion forums

Dringus, Laurie P.; Ellis, Tim

doi:10.1016/j.compedu.2004.05.003

Cited by 174 publications

(100 citation statements)

References 14 publications

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“…In this study, with the help of initial stage-setting guidelines, we saw students use the embedded analytics purposefully, making strategic decisions about where to post after seeing the big picture of the discussion and where they had participated in it. This may help to avoid some typical difficulties found in asynchronous online discussions, such as new post bias (Hewitt, 2003;Wise, Marbouti, Hsiao, & Hausknecht, 2012), shallow interaction with others' posts (Thomas, 2002), and not being able to see the discussion as a whole due to disorganized threading (Dringus & Ellis, 2005). It is certainly possible, however, for embedded analytics to have inadvertent undesirable effects as well.…”

Section: Discussionmentioning

confidence: 99%

Learning Analytics for Online Discussions: Embedded and Extracted Approaches

2014

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This paper describes an application of learning analytics that builds on an existing research program investigating how students contribute and attend to the messages of others in asynchronous online discussions. We first overview the E-Listening research program and then explain how this work was translated into analytics that students and instructors could use to reflect on their discussion participation. Two kinds of analytics were designed: some embedded in the learning environment to provide students with real-time information on their activity in-progress; and some extracted from the learning environment and presented to students in a separate digital space for reflection. In addition, we describe the design of an intervention though which use of the analytics can be introduced as an integral course activity. Findings from an initial implementation of the application indicated that the learning analytics intervention supported changes in students' discussion participation. Five issues for future work on learning analytics in online discussions are presented. One, unintentional versus purposeful change; two, differing changes prompted by the same analytic; three, importance of theoretical buy-in and calculation transparency for perceived analytic value; four, affective components of students' reactions; and five, support for students in the process of enacting analytics-driven changes.

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Section: Discussionmentioning

confidence: 99%

Learning Analytics for Online Discussions: Embedded and Extracted Approaches

2014

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“…Its methods include text mining that can work with unstructured or semi-structured data sets such as full-text documents, HTML files and emails. The specific application of text mining techniques in e-learning can be used for: grouping documents according to their topics and similarities and providing summaries (Hammouda & Kamel, 2006); finding and organizing material using semantic information (Tane et al, 2004); supporting editors when gathering and preparing the materials (Grobelnik, Mladenic, & Jermol, 2002); evaluating the progress of the thread discussion to see what the contribution to the topic is (Dringus & Ellis, 2005); collaborative learning and a discussion board with evaluation between peers (Ueno, 2004a); identifying the main blocks of multimedia presentations (Bari & Benzater, 2005); selecting articles and automatically constructing e-textbooks (Chen, Li, Wang, & Jia, 2004) and personalized courseware (Tang, Lau, Yin, Li, & Kilis, 2000); detecting the conversation focus of threaded discussions, classifying topics and estimating the technical depth of contribution (Kim, Chern, Feng, Shaw, & Hovy, 2006). -Outlier analysis (Hodge & Austin, 2004) is a type of data analysis that seeks to determine and report on records in the database that differ significantly from expectations.…”

Section: Other Techniquesmentioning

confidence: 99%

“…Instructors and course authors demand tools to assist them in this task, preferably on a continual basis. Although some platforms offer some reporting tools, it becomes hard for a tutor to extract useful information when there are a great number of students, (Dringus & Ellis, 2005). They do not provide specific tools allowing educators to thoroughly track and assess all learners' activities while evaluating the structure and contents of the course and its effectiveness for the learning process (Zorrilla, Menasalvas, Marin, Mora, & Segovia, 2005).…”

Section: Introductionmentioning

confidence: 99%

Data mining in course management systems: Moodle case study and tutorial

Romero

Ventura

García

2008

Computers & Education

797

334

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“…While analyzing log data to investigate learning effectiveness, Peled and Rashty (1999) found that the most popular online activities were, in general, passive activities, such as retrieving information, rather than contributing activities. Dringus and Ellis (2005) reported on how to analyze asynchronous discussion form usage data to evaluate the progress of a threaded discussion. Several recent studies showed a positive link between students' online activities and their final course grades.…”

Section: Behavioral Interactionsmentioning

confidence: 99%

Analysis of Time-on-Task, Behavior Experiences, and Performance in Two Online Courses with Different Authentic Learning Tasks

Park

2017

IRRODL

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This paper reports the findings of a comparative analysis of online learner behavioral interactions, timeon-task, attendance, and performance at different points throughout a semester (beginning, during, and end) based on two online courses: one course offering authentic discussion-based learning activities and the other course offering authentic design/development-based learning activities. Web log data were collected to determine the number of learner behavioral interactions with the Moodle learning management system (LMS), the number of behavioral interactions with peers, the time-on-task for weekly tasks, and the recorded attendance. Student performance on weekly tasks was also collected from the course data.Behavioral interactions with the Moodle LMS included resource viewing activities and uploading/downloading file activities. Behavioral interactions with peers included discussion postings, discussion responses, and discussion viewing activities. A series of Mann-Whitney tests were conducted to compare the two types of behavioral interactions between the two courses. Additionally, each student's behavioral interactions were visually presented to show the pattern of their interactions. The results indicated that, at the beginning of the semester, students who were involved in authentic design/development-based learning activities showed a significantly higher number of behavioral interactions with the Moodle LMS than did students involved in authentic discussion-based learning activities. However, in the middle of the semester, students engaged in authentic discussion-based learning activities showed a significantly higher number of behavioral interactions with peers than did students involved in authentic design/development-based learning activities. Additionally, students who were given authentic design/development-based learning activities received higher performance scores both during the semester and at the end of the semester and they showed overall higher performance scores than Analysis of Time-on-Task, Behavior Experiences, and Performance in Two Online Courses with Different Authentic Learning Tasks Park 214 students who were given authentic discussion-based learning activities. No differences were found between the two groups with respect to time-on-task or attendance.

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Using data mining as a strategy for assessing asynchronous discussion forums

Cited by 174 publications

References 14 publications

Learning Analytics for Online Discussions: Embedded and Extracted Approaches

Learning Analytics for Online Discussions: Embedded and Extracted Approaches

Data mining in course management systems: Moodle case study and tutorial

Analysis of Time-on-Task, Behavior Experiences, and Performance in Two Online Courses with Different Authentic Learning Tasks

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